In this study, we focus on the development and implementation of a comprehensive ensemble of numerical time series forecasting models, collectively referred to as the Group of Numerical Time Series Prediction Model (G-NM). This inclusive set comprises traditional models such as Autoregressive Integrated Moving Average (ARIMA), Holt-Winters' method, and Support Vector Regression (SVR), in addition to modern neural network models including Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). G-NM is explicitly constructed to augment our predictive capabilities related to patterns and trends inherent in complex natural phenomena. By utilizing time series data relevant to these events, G-NM facilitates the prediction of such phenomena over extended periods. The primary objective of this research is to both advance our understanding of such occurrences and to significantly enhance the accuracy of our forecasts. G-NM encapsulates both linear and non-linear dependencies, seasonalities, and trends present in time series data. Each of these models contributes distinct strengths, from ARIMA's resilience in handling linear trends and seasonality, SVR's proficiency in capturing non-linear patterns, to LSTM's adaptability in modeling various components of time series data. Through the exploitation of the G-NM potential, we strive to advance the state-of-the-art in large-scale time series forecasting models. We anticipate that this research will represent a significant stepping stone in our ongoing endeavor to comprehend and forecast the complex events that constitute the natural world.

翻译：在本研究中，我们聚焦于开发并实现一个综合性的数值时间序列预测模型集成，统称为数值时间序列预测模型组（G-NM）。该集成集合包括传统模型，如自回归积分滑动平均模型（ARIMA）、霍尔特-温特斯方法及支持向量回归（SVR），以及现代神经网络模型，包括循环神经网络（RNN）和长短期记忆网络（LSTM）。G-NM旨在增强我们对复杂自然现象中固有模式与趋势的预测能力。通过利用与这些现象相关的时间序列数据，G-NM能够促进对这类现象进行长期预测。本研究的主要目标既在于深化我们对这些现象的理解，亦在于显著提升预测精度。G-NM涵盖了时间序列数据中的线性和非线性依赖关系、季节性和趋势。从ARIMA在处理线性趋势和季节性方面的稳健性，到SVR在捕捉非线性模式方面的特长，再到LSTM在建模时间序列数据各组成部分中的适应性，每个模型均贡献其独特优势。通过挖掘G-NM的潜力，我们致力于推动大规模时间序列预测模型的先进水平。我们预期，这项研究将成为我们持续探索和理解构成自然世界的复杂事件征程中的重要里程碑。